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We consider in this article battery state of power (SoP) estimation, in particular, we propose two algorithms for predicting voltage corresponding to a future current profile that is known to be demanded by the battery load. The proposed algorithms belong to the class of data-driven methods and are based on the Gaussian Process Regression (GPR) framework. In comparison to conventional model-based...
This paper presents an application of Gaussian process regression (GPR) to estimate a state of charge (SoC) of Lithium-ion (Li-ion) batteries with different kernel functions. One of the practical advantages of using GPR is that uncertainties in the estimates can be quantified, which enables reliability assessment of the SoC estimate. The inputs of GPR are voltage, current and temperature measurements...
This paper presents a new online method for state-of charge (SoC) estimation of Lithium-ion (Li-ion) batteries based on sparse Gaussian process regression (GPR). Building upon sparse approximation of the regular GPR, the proposed method is computationally more efficient. The battery SoC is estimated based on measured voltage, current and temperature. The accuracy of the proposed method is verified...
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